Method and Apparatus for Automated Impact Analysis

Abstract

A method and system for automatically analyzing the impact of a treatment of interest is disclosed. Data related to a treatment of interest and a population including a treated group and a non-treated group is received. Propensity scores are estimated for the treated group and the non-treated group. Subgroups of the treated group and the non-treated group are matched based on the propensity scores. An outcome model is generated for each subgroup of the non-treated group, and an impact of the treatment on the treated group is generated for each subgroup of the treated group using the outcome model generated for the matching subgroup of the control group. Outcome models may be generated for the treated group and the non-treated group, and an impact of the treatment on the population may be generated based on the propensity scores and the outcome models for the test group and the non-treated group.

Description

BACKGROUND OF THE INVENTION[0001]The present invention relates to analyzing the impact of a particular feature or service, and more particularly, to automatically automated impact analysis of services and features on on-line advertisers.[0002]An on-line advertising system may provide advertisements to users when they visit certain web pages. When a particular advertisement is of interest to a user, the user may perform various actions, such as selecting or clicking on the advertisement, which may take the user to a web page belonging to the advertiser associated with the advertisement. Additional examples of user actions may include signing-up for services at the target web page, placing an order, etc. On-line advertising systems may charge advertisers based, at least in part, on a number of clicks an advertisement receives.[0003]On-line advertising systems continually develop and implemented new features and services for advertisers. It is important to identify whether such new fea...

AI Technical Summary

Benefits of technology

The present invention offers an automatic way to measure the effects of a treatment applied to a group of people. It can account for various factors that might influence the results, such as selection bias, seasonality, and economic cycle. The method involves estimating propensity scores for a treatment and a control group, and then matching subgroups of the treated and non-treated groups based on the scores. Outcome models are generated for each subgroup, and the impact of the treatment on the treated group is estimated based on the models. This technique can provide a valuable tool for evaluating the effectiveness of treatments in a population.

Problems solved by technology

However, properly attributing cause-effect relationships related to a particular feature or service is typically difficult in the presence of confounding factors that can lead to false attribution of cause and effect.

For example, many issues, such as selection bias of the advertisers who have received the benefit of the feature or service, seasonality, and economic cycle make it difficult to accurately analyze the actual impact of the particular feature or service.

In many cases, traditional randomized controlled experiment designs are not realistic for analyzing the impact of a particular good or service.

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